AutoML Market From $346. 2 million in 2020, the automated machine learning market is predicted to reach $14,830. 8 million by 2030, demonstrating a CAGR of 45. 6% from 2020 to 2030.

New York, Dec. 16, 2021 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "AutoML Market" - https://www.reportlinker.com/p06191010/?utm_source=GNW The major factors driving the market are the burgeoning requirement for efficient fraud detection solutions, soaring demand for personalized product recommendations, and increasing need for predictive lead scoring.

The COVID-19 pandemic has contributed significantly to the evolution of digital business models, with many healthcare companies adopting machine-learning-enabled chatbots to enable the contactless screening of COVID-19 symptoms. Moreover, Clevy.io, which is a France-based start-up, and Amazon Web Services (AWS) have launched a chatbot for making the process of finding official government communications about the COVID-19 infection easy. Thus, the pandemic has positively impacted the market.

The service category, under the offering segment, is predicted to demonstrate the faster growth in the coming years. This is credited to the burgeoning requirement for implementation and integration, consulting, and maintenance services, as they assist in enhancing business productivity and augmenting coding activities. Additionally, these services aid in automating workflows, which, in turn, enables the mechanization of complex operations.

The cloud category dominated the AutoML market, within the deployment type segment, in the past. Moreover, this category is predicted to grow rapidly in the forthcoming years on account of the flexibility and scalability provided by cloud-based automated machine learning (AutoML) solutions.

Geographically, North America held the largest share in the past, and this trend is expected to continue in the coming years. This is credited to the soaring venture capital funding by artificial intelligence (AI) companies for research and development (R&D), in order to advance AutoML.

Asia-Pacific (APAC) is predicted to be the fastest-growing region in the market in the forthcoming years. This is ascribed to the growing information technology (IT) investments and increasing fintech adoption in the region. In addition, the growing government focus on incorporating AI in multiple verticals is supporting the advance of the market in the region.

For instance, in October 2021, Hivecell, which is an edge as a service company, entered into a partnership with DataRobot Inc. for solving bigger challenges and hurdles at the edge, by processing various ML models on site and outside the data closet. By incorporating the two solutions, businesses can make data-driven decisions more efficiently.

The major players in the AutoML market are DataRobot Inc., dotData Inc., H2O.ai Inc., Amazon Web Services Inc., Big Squid Inc., Microsoft Corporation, Determined.ai Inc., SAS Institute Inc., Squark, and EdgeVerve Systems Limited.Read the full report: https://www.reportlinker.com/p06191010/?utm_source=GNW

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The automated machine learning market is predicted to reach $14,830.8 million by 2030, demonstrating a CAGR of 45.6% from 2020 to 2030 - Yahoo Finance

SAN FRANCISCO--(BUSINESS WIRE)--Continual, a company building a next-generation AI platform for the modern data stack, today announces its public beta launch with $4 million in seed funding. The round was led by Amplify Partners, a firm that invests in companies with a vision of transforming infrastructure and machine intelligence tools. Illuminate Ventures, Essence, Wayfinder, and Data Community Fund also participated in the round.

The modern data stack centered on cloud data warehouses like Snowflake is rapidly democratizing data and analytics, but deploying AI at scale into business operations, products, or services remains a challenge for most companies. Powered by a declarative approach to operational AI and end-to-end automation, Continual enables modern data and analytics teams to build continually improving machine learning models directly on their cloud data warehouse without complex engineering.

Continual brings together second time founders Tristan Zajonc and Tyler Kohn who previously built and sold machine learning infrastructure startups. Cofounder and CEO Tristans first startup, Sense, a pioneering enterprise data science platform, was acquired by Cloudera in 2016. Continuals cofounder and CTO, Tyler Kohn, built RichRelevance, the worlds leading personalization provider, before it was acquired by Manthan in 2019. Tristan and Tyler saw the huge gap between the transformational potential of AI and the day-to-day struggle most companies faced operationalizing AI using real world data. They founded Continual to radically simplify operational AI by taking a fundamentally new approach.

Artificial intelligence has the potential to transform every industry, department, product and service but current solutions require complex infrastructure, advanced skills, and constant maintenance. Continual breaks through this complexity with a radical simplification of the machine learning development lifecycle, combining a declarative approach to operational AI, end-to-end automation, and the agility of the modern data stack. Our customers are deploying state-of-art predictive models that never stop learning from their data in minutes rather than months, said Tristan Zajonc, CEO and cofounder of Continual.

Getting continually improving predictive insights from data is critical for businesses to operate efficiently and better serve their customers. Yet operationalizing AI remains a challenge for all but the most sophisticated companies, said David Beyer, Partner at Amplify Partners. Continual meets data teams where they work - inside the cloud data warehouse - and lets them build and deploy continually improving predictive models in a fraction of the time existing approaches demand. We invested because we believe their approach is fundamentally new and, most importantly, the right one to make AI work across the enterprise."

With the new capital, Continual plans to more than double its team over the next year with new hires for sales and engineering roles. It will expand into new AI/ML use cases such as NLP, realtime, and personalization, and broaden support for additional cloud data platforms. Continual is offering a 14-day trial with its open beta release, enhancements for dbt users, and support for Snowflake, Redshift, BigQuery, and Databricks.

dbt was built on the idea that the unlock for data teams is a collaborative workflow that brings more people into the knowledge creation process. Continual brings this same viewpoint to machine learning, adding new capabilities to the analytics engineers' tool belt, said Nikhil Kothari, Head of Technology Partnerships at dbt Labs. Were excited to partner with Continual to help bring operational AI to the dbt community.

Continual is enabling organizations to easily build, deploy, and maintain continually improving predictive models directly on top of Snowflake, said Tarik Dwiek, Head of Technology Alliances at Snowflake. As part of our partnership, were excited to help bring these benefits to the Snowflake community and to accelerate end-to-end machine learning workflows on top of Snowflake with Snowpark.

To learn more about Continual or to sign up for a 14-day trial, visit: https://continual.ai

About Continual

Based in San Francisco, Continual is a next-generation AI platform for the modern data stack powered by end-to-end automation and a declarative workflow. Modern data teams use Continual to deploy continually improving predictive models to drive revenue, operate more efficiently, and power innovative products and services. Continual has raised $4 million in funding from Amplify Partners, Illuminate Ventures, Essence, Wayfinder, and Data Community Fund. For more information, visit https://continual.ai/

About Amplify Partners

Amplify Partners invests in early-stage companies pioneering novel applications in machine intelligence and computer science. The firm's deep domain expertise, unrivaled relationships with leading technologists and decades of operational experience, positions it uniquely with enterprise insight and the ability to serve technical founding teams. To learn more about Amplify's portfolio and people, please visit amplifypartners.com.

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Continual Launches With $4 Million in Seed to Bring AI to the Modern Data Stack - Business Wire

Rapidly rising demand for Internet connectivity has put a strain on improving network infrastructure, performance, and other critical parameters. Network administrators will invariably encounter different types of networks running multiple network applications. Each network application has its own set of features and performance parameters that may change dynamically. Because of the diversity and complexity of networks, using conventional algorithms or hard-coded techniques built for such network scenarios is a challenging task.

Machine learning has proven to be beneficial in almost every industry, and the networking industry is no exception. Machine learning can help solve the intractable old networking blockers and stimulate new network applications that make networking quite convenient. Lets discuss in detail the basic workflow, with a few use cases to better understand applied machine learning technology in the networking domain.

With the growing demand for Internet of Things (IoT) solutions, modern networks generate massive and heterogeneous traffic data. For such a dynamic network, the traditional network management techniques for network traffic monitoring and data analytics like ping monitoring, Logfile monitoring, or even SNMP are not enough. They usually lack accuracy and effective processing of real-time data. On the other hand, traffic from other sources like cellular or mobile devices in the network comparatively shows a more complex behavior due to device mobility and network heterogeneity.

Machine learning facilitates analytics in big data systems as well as large-area networks to recognize complex patterns when it comes to managing such networks. Looking at these opportunities, researchers in the field of networking use deep learning models for Network Traffic Monitoring and Analysis applications like traffic classification and prediction, congestion control, etc.

Network telemetry data provides basic metrics about network performance. This information is usually quite difficult to interpret. Considering the size and the total data going through the network, the analyzed data holds tremendous value. If used smartly, it can drastically improve performance.

Emerging technologies like Inband-Network Telemetry can help when collecting detailed network telemetry data in real-time. On top of that, running machine learning on such datasets can help correlate phenomena between latency, paths, switches, routers, events, etc. These phenomena were difficult to point out from the enormous amounts of real-time data using the traditional methods.

Machine learning models are trained to understand correlations and patterns in the telemetry data. These algorithms then eventually gain the ability to predict the future based on learning from historical data. This helps in managing future network outages.

Every network infrastructure has a predefined total throughput available. It is further split into multiple lanes of different predefined bandwidths. In such scenarios, where the total bandwidth usage for each end-user is statically predefined, there can be bottlenecks for some parts of the network where the network is overwhelmingly used.

To avoid such congestion supervised machine learning models can be trained to analyze network traffic in real-time and infer a suitable amount of bandwidth per user in such a way that the network experiences the least amount of bottlenecks.

Such models can learn from the network statistics such as total active users per network node, historical network usage data for each user, time-based patterns of data usage, movement of users across multiple access points, and so on.

In each network, there exists various kinds of traffic like Web Hosting (HTTP), File transfers (FTP), Secure Browsing (HTTPS), HTTP Live Video Streaming (HLS), Terminal Services (SSH), and so on. Each of these behaves differently when it comes to network bandwidth usage; for example, transferring a file over FTP uses a lot of data continuously for the duration of the transfer.

As another example, if a video is being streamed, it uses the data in chunks and a buffering method. These different types of traffic, when allowed to use the network in an unsupervised way, create some temporary blockages.

To avoid this, machine learning classifiers can be used which can analyze and classify the type of traffic going through the network. These models can then be used to infer network parameters like allocated bandwidth, data caps, etc., which can in turn help improve the performance of the network by improving the scheduling of requests served and also dynamically changing the assigned bandwidths.

The increase in the number of cyberattacks forces organizations to constantly monitor and correlate millions of external and internal data points across the whole network infrastructure and its users. Manual management of a large volume of real-time data becomes difficult. This is where machine learning helps.

Machine learning can recognize certain patterns and anomalies in the network and predict threats in massive data sets, all in real-time. By automating such analysis, it becomes easy for network managers to detect threats and isolate situations rapidly with reduced human efforts.

Network behavior is an important parameter in machine learning systems for anomaly detection. Machine learning engines process enormous amounts of data in real-time to identify threats, unknown malware, and policy violations.

If the network behavior is found to be within the predefined behavior, the network transaction is accepted; otherwise, an alert gets triggered in the system. This can be used to prevent many kinds of attacks like DoS, DDoS, and probing.

Its quite easy to trick someone into clicking a malicious link that seems legitimate, then try to break through a computers defense systems with the information gathered. Machine learning helps in flagging suspicious websites to help prevent people from connecting to malicious websites.

For example, a text classifier machine learning model can read and understand URLs and identify those spoofed phishing URLs. This will create a much safer browsing experience for the end-users.

The integration of machine learning in networking is not limited to the above-mentioned use cases. Solutions can be developed in the field of using ML for networking and network security to solve the unaddressed issues by shedding light on the opportunities and research from both the networking and machine learning perspectives.

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Real World Application of Machine Learning in Networking - IoT For All

With 90 percent of businesses trying to use machine learning, it's time to reconsider the technology's true benefits and capabilities.

Fremont, CA:The complexity of infrastructure or workload requirements is the greatest difficulty organizations confront when using machine learning. A whopping 90 percent of CXOs share this sentiment. To get into the specifics, 88 percent of respondents say they have trouble integrating AI/ML technology, and 86 percent say they have trouble keeping up with the regular changes necessary for data science tools.

Every year, certain technologies gain a greater level of popularity than others. Cloud computing, big data, and cybersecurity are examples of this. Machine learning is now the talk of the town that inspires people to fantasize about the future and the possibilities that it may bring. Even more terrifying are the nightmares, which depict self-learning robots capable of taking over the globe. However, the reality is a long cry from this. It is challenging to understand how statistical and mathematical supervised learning models are used nowadays in machine learning.

Such future visions undoubtedly push us to invest in technology, but they also fuel the so-called hype. According to experts, such scenarios happen when ML gets asked without first addressing the internal data ready or the tool's needs.

It is critical to establish a robust foundation of data for successful project execution when using machine learning, and it necessitates a complete shift in organizational culture and processes.

Before any machine learning development can begin, companies must first focus on 'data readiness.' It entails obtaining clean and consistent data and developing data governance processes and scalable data architectures. Firms must execute long-term data-based plans and policies to build a unified data architecture.

Employees need time to adjust to new technology, and machine learning is no exception.

When computers first became prominent in 1950, many people believed that the future of these robots would be humanoids, particularly in the military. Nobody anticipated, however, that the Internet would genuinely transform the world. Today's scenario is similar, with the latest AI and machine learning algorithms always being overhyped.

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Reasons behind the Current Hype Around Machine Learning - CIO Applications

By Columbia University School of Engineering and Applied ScienceDecember 12, 2021

Schematic of the bridging of the cold quantum world and high-temperature metal extraction with machine learning. Credit: Rodrigo Ortiz de la Morena and Jose A. Garrido Torres/Columbia Engineering

Method combines quantum mechanics with machine learning to accurately predict oxide reactions at high temperatures when no experimental data is available; could be used to design clean carbon-neutral processes for steel production and metal recycling.

Extracting metals from oxides at high temperatures is essential not only for producing metals such as steel but also for recycling. Because current extraction processes are very carbon-intensive, emitting large quantities of greenhouse gases, researchers have been exploring new approaches to developing greener processes. This work has been especially challenging to do in the lab because it requires costly reactors. Building and running computer simulations would be an alternative, but currently there is no computational method that can accurately predict oxide reactions at high temperatures when no experimental data is available.

A Columbia Engineering team reports that they have developed a new computation technique that, through combining quantum mechanics and machine learning, can accurately predict the reduction temperature of metal oxides to their base metals. Their approach is computationally as efficient as conventional calculations at zero temperature and, in their tests, more accurate than computationally demanding simulations of temperature effects using quantum chemistry methods. The study, led by Alexander Urban, assistant professor of chemical engineering, was published on December 1, 2021 by Nature Communications.

Decarbonizing the chemical industry is critical if we are to transition to a more sustainable future, but developing alternatives for established industrial processes is very cost-intensive and time-consuming, Urban said. A bottom-up computational process design that doesnt require initial experimental input would be an attractive alternative but has so far not been realized. This new study is, to our knowledge, the first time that a hybrid approach, combining computational calculations with AI, has been attempted for this application. And its the first demonstration that quantum-mechanics-based calculations can be used for the design of high-temperature processes.

The researchers knew that, at very low temperatures, quantum-mechanics-based calculations can accurately predict the energy that chemical reactions require or release. They augmented this zero-temperature theory with a machine-learning model that learned the temperature dependence from publicly available high-temperature measurements. They designed their approach, which focused on extracting metal at high temperatures, to also predict the change of the free energy with the temperature, whether it was high or low.

Free energy is a key quantity of thermodynamics and other temperature-dependent quantities can, in principle, be derived from it, said Jos A. Garrido Torres, the papers first author who was a postdoctoral fellow in Urbans lab and is now a research scientist at Princeton. So we expect that our approach will also be useful to predict, for example, melting temperatures and solubilities for the design of clean electrolytic metal extraction processes that are powered by renewable electric energy.

The future just got a little bit closer, said Nick Birbilis, Deputy Dean of the Australian National University College of Engineering and Computer Science and an expert for materials design with a focus on corrosion durability, who was not involved in the study. Much of the human effort and sunken capital over the past century has been in the development of materials that we use every day and that we rely on for our power, flight, and entertainment. Materials development is slow and costly, which makes machine learning a critical development for future materials design. In order for machine learning and AI to meet their potential, models must be mechanistically relevant and interpretable. This is precisely what the work of Urban and Garrido Torres demonstrates. Furthermore, the work takes a whole-of-system approach for one of the first times, linking atomistic simulations on one end engineering applications on the other via advanced algorithms.

The team is now working on extending the approach to other temperature-dependent materials properties, such as solubility, conductivity, and melting, that are needed to design electrolytic metal extraction processes that are carbon-free and powered by clean electric energy.

Reference: Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures by Jose Antonio Garrido Torres, Vahe Gharakhanyan, Nongnuch Artrith, Tobias Hoffmann Eegholm and Alexander Urban, 1 December 2021, Nature Communications.DOI: 10.1038/s41467-021-27154-2

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Quantum Mechanics and Machine Learning Used To Accurately Predict Chemical Reactions at High Temperatures - SciTechDaily